Bibliographic record
Abstract
The aim of the thesis is to describe the demographics of Czech PR professionals, the educational background of these workers, the original profession from which they came to public relations, and their job description. The last research question is satisfaction with their career choice and opinions on the conditions for success in this profession. The paper is based on an anonymous online questionnaire survey of 463 PR professionals from the Czech Republic in autumn 2022. The questionnaire was based on the regular UK CIPR State of the Profession survey and the European Communication Monitor questionnaire. Based on the data, it was found that the average age of Czech communication professionals is 40 years, 87% of them have a university degree, more than half of them in social sciences. Most of the respondents came to PR from another field, most often from the media, and for a quarter of the workers, working in PR is their first profession. Regardless of position or years of experience, the most common activity of all PR professionals is media relations, followed by communication strategies building, and the third most common is copywriting. Most respondents devote more than 60% of their time to PR. If their position is complemented by another role, it is marketing. Among the respondents, 60% were women,...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".